In [7]:
from sktime.performance_metrics.forecasting import MeanSquaredError
rmse = MeanSquaredError(square_root=True)
for col in y_train.columns:
plot_series(y_train[col], y_test[col], y_pred[col], labels=['y_train', 'y_test', 'y_pred'])
print('RMSE for {}: {}'.format(col, rmse(y_test[col], y_pred[col])))
print('Overall RMSE {}'.format(rmse(y_test,y_pred)))
RMSE for AUTOMOTIVE: 1.358381001891627 RMSE for BABY CARE: 0.0969352019679599 RMSE for BEAUTY: 1.6163742516734927 RMSE for BEVERAGES: 629.8133697253157 RMSE for BOOKS: 0.02191140676732531 RMSE for BREAD/BAKERY: 54.638165398252255 RMSE for CELEBRATION: 2.3688554957858448 RMSE for CLEANING: 581.5756684629596 RMSE for DAIRY: 95.19699760691029 RMSE for DELI: 41.212282499624564 RMSE for EGGS: 20.67250901534757 RMSE for FROZEN FOODS: 12.506054721096808 RMSE for GROCERY I: 791.7376935103116 RMSE for GROCERY II: 11.5325489717349 RMSE for HARDWARE: 0.19580651176327848 RMSE for HOME AND KITCHEN I: 5.854356140667163 RMSE for HOME AND KITCHEN II: 7.427266496125442 RMSE for HOME APPLIANCES: 0.15180667666099854 RMSE for HOME CARE: 78.19120871893784 RMSE for LADIESWEAR: 1.791976582463979 RMSE for LAWN AND GARDEN: 1.8161242622125413 RMSE for LINGERIE: 2.289471234711228 RMSE for LIQUOR,WINE,BEER: 22.58739890864535 RMSE for MAGAZINES: 2.4666501000439687 RMSE for MEATS: 37.824619704418026 RMSE for PERSONAL CARE: 76.6610270107757 RMSE for PET SUPPLIES: 1.4669191549337932 RMSE for PLAYERS AND ELECTRONICS: 1.7508374533172635 RMSE for POULTRY: 45.92946889127437 RMSE for PREPARED FOODS: 6.923388492262947 RMSE for PRODUCE: 227.95699803772067 RMSE for SCHOOL AND OFFICE SUPPLIES: 47.81117891504797 RMSE for SEAFOOD: 3.299214022617051 Overall RMSE 85.35586256315877
